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Gene-Environment Interactions in Complex Diseases. Jeppe Madura Larsen, MSc , PhD Assistant Professor. Life expectancy increase. Advances. Nutrition, food availability Living conditions, urbanization Universal health care Vaccines: Polio, HepB /A, small pox

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gene environment interactions in complex diseases

Gene-EnvironmentInteractions in ComplexDiseases

Jeppe Madura Larsen, MSc, PhD

Assistant Professor

advances
Advances
  • Nutrition, food availability
  • Living conditions, urbanization
  • Universal health care
  • Vaccines: Polio, HepB/A, small pox
  • Surgery: Transfusion, transplantation, technology
  • Medicine: Penicillin, steriods, chemotherapy
on the flip side what doesn t kill us fast
On the flip side:what doesn’t kill us... (fast)
  • Asthma

Eder et al, N Engl J Med, 2006

on the flip side what doesn t kill us fast1
On the flip side:what doesn’t kill us... (fast)
  • Asthma
  • Hayfever, eczema

Latvala et al, BMJ, 2005

on the flip side what doesn t kill us fast2
On the flip side:what doesn’t kill us... (fast)
  • Asthma
  • Hayfever, eczema
  • Obesity, T2D

Kavey et al, Pediatrics, 2011

on the flip side what doesn t kill us fast3
On the flip side:what doesn’t kill us... (fast)
  • Asthma
  • Hayfever, eczema
  • Obesity, T2D
  • Autoimmune diseases: IBD, RA, MS
  • Cancer
societal challenges
Societal challenges
  • Patient morbidity
  • Social-economic impact
  • Health care expenditures
genetics gwas
Genetics: GWAS
  • Define genotypes associated/predictive of disease

Manhattan plot

genetics gwas findings
Genetics: GWAS findings
  • Several disease associated loci found

Colitis (McGovern el. al., Nat Gen, 2010)

genetics gwas findings1
Genetics: GWAS findings
  • Several disease associated loci found

BMI & T2D (O’Rahilly el. al., Nature, 2009)

genetics gwas findings2
Genetics: GWAS findings
  • Several disease associated loci found
  • However:
    • Frequently major alleles associates with disease
    • Low disease predictive value
    • In T1D: 30 % heritability explained
    • In T2D: 1 % heritability explained
  • No single SNP is clearly associates with disease. However, several SNPs may collectively contribute to disease via a common pathway.
gwas challenge extracting disease genotypes
GWAS challenge:Extracting disease “genotypes”
  • Integrating GWAS data with:
    • Protein-protein interaction
    • Protein function
    • Metabolic pathway
    • Cell/tissue specificity
    • Cell interaction
  • Future
    • Repetitive DNA
    • Copy-number variants
    • Epigenetics: DNA/histonemethylation
the environmental factors
The environmental factors
  • Likely accountable for recent rise in disease prevalence
  • Act on genetic predisposition
the environmental factors in childhood asthma
The environmental factorsin childhood asthma
  • Several diverse factors
    • Living on a farm/rural area (increased bacterial diversity or microbial products)
    • Airway microbiota composition
    • Nutrition (vitamin D, PUFA)
    • Parental smoking
    • C-section
    • Birth order
    • Siblings in home
    • Pets
  • A role for both peri-natal and natal exposures
overview shaping disease risk
Overview: Shaping disease risk

Renz et. al., Nat Imm, 2011

translational research
Translational research

Guo & Zakhari, NIAAA

challenges for the clinic
Challenges for the clinic
  • Disease definitions are likely inadequate
  • Define disease phenotypes/endotypes:

“Endotype—a contraction of endophenotype—is a subtype of disease defined functionally and pathologically by a molecular mechanism or by treatment response. Asthma, like many chronic disorders, is a heterogeneous and genetically complex disease, meaning that many genes (>100 have been identified) are likely to contribute, variably, to its different manifestations. Asthma is likely to have several specific endotypes associated with distinct clinical features, divergent underlying molecular causes, and distinct treatment responses.”

(Anderson, Lancet, 2008)

  • A need for additional objective and quantitative parameters
  • Standard treatment algorithms/guidelines
  • Run large cohorts for studies of disease development and preventive intervention
challenges for basic science
Challenges for basic science
  • Sampling and measuring environmental factors
  • Develop/improve HTS methods in-depth genetic and biochemical characterization
  • Translate human findings into focused disease relevant animal models for pharmacological development
challenges for systems biology
Challenges for systems biology
  • Develop methods for integration of new datasets
  • Develop standardized data structures, data handling and pipelines
  • Data sharing (both in academia and industry)
  • Model development and validation. Unrestricted of previous disease definitions. To be tested in the clinic and/or animal models.
litterature
Litterature
  • European Science Foundation, rapport 2011. Forward Look: Gene-environment interaction in Chronic Disease.
  • Renz et. al., JACI, 2011. Gene-environment interaction in Chronic Disease.
  • Renz et. al., Nat. Imm., 2011. Gene-environment interaction in Chronic Inflammatory Disease.